Senior Data Engineer - Streaming Platform

Summary

Join Voodoo, a leading mobile game and app developer, as a Data Engineer specializing in real-time data pipelines. You will be part of the Ad-Network Team, a group of engineers dedicated to optimizing advertising within a real-time bidding environment. This role involves designing, implementing, and optimizing data pipelines handling billions of events per day, ensuring high-throughput, low-latency processing, and system resilience. You will work with technologies like Kafka, Flink, Spark Structured Streaming, and Kubernetes, collaborating with backend teams to integrate OpenRTB signals into the data platform. This position offers the opportunity to mentor other engineers, contribute to data modeling and retention strategies, and stay at the forefront of modern streaming technologies.

Requirements

• Extensive experience in data or backend engineering, with at least 2+ years building real-time data pipelines
• Proficiency with stream processing frameworks like Flink, Spark Structured Streaming, Beam, or similar
• Strong programming experience in Java, Scala, or Python, with a focus on distributed systems
• Deep understanding of event streaming and messaging platforms such as GCP Pub/Sub, AWS Kinesis, Apache Pulsar, or Kafka — including performance tuning, delivery guarantees, and schema management
• Solid experience operating data services in Kubernetes, including Helm, resource tuning, and service discovery
• Experience with Protobuf/Avro, and best practices around schema evolution in streaming environments
• Familiarity with CI/CD workflows and infrastructure-as-code (e.g., Terraform, ArgoCD, CircleCI)
• Strong debugging skills and a bias for building reliable, self-healing systems

Responsibilities

• Design, implement, and optimize real-time data pipelines handling billions of events per day with strict SLAs
• Architect data flows for bidstream data, auction logs, impression tracking and user behavior data
• Build scalable and reliable event ingestion and processing systems using Kafka, Flink, Spark Structured Streaming, or similar technologies
• Operate data infrastructure on Kubernetes, managing deployments, autoscaling, resource limits, and high availability
• Collaborate with backend to integrate OpenRTB signals into our data platform in near real-time
• Ensure high-throughput, low-latency processing, and system resilience in our streaming infrastructure
• Design and manage event schemas (Avro, Protobuf), schema evolution strategies, and metadata tracking
• Implement observability, alerting, and performance monitoring for critical data services
• Contribute to decisions on data modeling and data retention strategies for real-time use cases
• Mentor other engineers and advocate for best practices in streaming architecture, reliability, and performance
• Continuously evaluate new tools, trends, and techniques to evolve our modern streaming stack

Preferred Qualifications

• Knowledge of stream-native analytics platforms (e.g., Druid, ClickHouse, Pinot)
• Understanding of frequency capping, fraud detection, and pacing algorithms
• Exposure to service mesh, auto-scaling, and cost optimization in containerized environments
• Contributions to open-source streaming or infra projects

Benefits

• Best-in-class compensation
• Other benefits according to the country you reside in